Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
An Analysis of Koza's Computational Effort Statistic for Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Towards scalable genetic programming
Towards scalable genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Confidence intervals for computational effort comparisons
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Parallel evolution using multi-chromosome cartesian genetic programming
Genetic Programming and Evolvable Machines
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Achieving COSMOS: a metric for determining when to give up and when to reach for the stars
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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This paper analyses the reliability of confidence intervals for Koza's computational effort statistic. First, we conclude that dependence between the observed minimum generation and the observed cumulative probability of success leads to the production of more reliable confidence intervals for our preferred method. Second, we show that confidence intervals from 80% to 95% have appropriate levels of performance. Third, simulated data is used to consider the effect of large minimum generations and the confidence intervals are again found to be reliable. Finally, results from four large datasets collected from real genetic programming experiments are used to provide even more empirical evidence that the method for producing confidence intervals is reliable.